1. Technical Field
The disclosed embodiments relate in general to a signal processing method and a signal processing apparatus.
2. Description of the Related Art
Referring to FIGS. 10-16. FIG. 10 shows a 10 Hz sine wave. FIG. 11 shows a sine wave of 2 Hz. FIG. 12 shows a trend line. FIG. 13 shows a mixture of 10 Hz and 2 Hz waves. FIG. 14 shows a corresponding spectrum of FIG. 13. FIG. 15 shows a trend signal and a mixture of 10 Hz and 2 Hz waves with the trend signal. FIG. 16 shows a corresponding spectrum of FIG. 15. The horizontal coordinate of FIGS. 10-15 denotes the time index of a signal, and the vertical coordinate of FIGS. 10-15 denotes the magnitude of the signal. The horizontal coordinate of FIGS. 14 and 16 denotes the frequency (Hz) of the signal, and the vertical coordinate of FIGS. 14 and 16 denotes the amplitude of the signal spectrum. The physiological signal is normally captured through an electrode, and further intensified and amplified to an acceptable range by a front-end amplifier. Then, the signal of the interference source is filtered by a filter to obtain a signal of interest. Lastly, the analog signal is converted into a digital signal for subsequent processing by an analog-to-digital converter. The specification of the capturing parameter, the design of the filter and the design of the rear-end hardware such as recorder, alarm system and wireless transmission are dependent on the application.
Natural signals normally contain the information of various time scales, which may cover different frequency bands, have different energy distributions and specific wave patterns. These signals are basically divided into the periodic component and the aperiodic component, wherein the aperiodic component, such as denoting the trend, discontinuity, and stochasticity. When such signals are analyzed by fast Fourier transform (FFT), the spectrum may be contaminated by the aperiodic trend line. As illustrated in FIGS. 13-16, the trend line not only affects the low-frequency part of the spectrum, but also affects the entire spectrum. Such spectrum problems cannot be resolved by conventional filtering method. The influence on the spectrum by the trend can only be relieved by analyzing the time signal.
The trend indicates the overall tendency of the signal, while the other periodic components are the fluctuation overriding the trend signal. The magnitude of the fluctuation of the signal cannot be easily obtained without prior understanding of the trend. On the other hand, if the trend signal is not processed properly, the spectrum will be contaminated and subsequent signal processing will be troublesome.
When developing the application of physiological signal technology, the capturing of the physiological signal must be resolved first before the characteristics of the signal can be analyzed. However, the same physiological signal applied in different applications requires different signal processing methods to perform better. In the analysis of electrocardiography signal, for example, the properties of signal distribute wildly from high to low frequency. For the application of heart rate measurement, one can filter out all components except the QRS complex. Although the filtered signal is distorted significantly, the heart rate still can be effectively monitored as long as R waves are recognizable. When it comes to monitoring the variation in particular wave patterns such as ST-elevations and the variation in the QT intervals, careful filtering is required to assure the recognizability of these specific wave patterns. For example, low-frequency baseline wandering may easily affect the wave pattern of low-frequency T wave, and make the wave pattern unrecognizable.
On the other hand, when the conventional FFT method is used for calculating the spectrum, the input signal is assumed to repeat periodically and the periodic length is equal to the length of the signal. However, if the true signal is not periodic or the assumed periodic length is incorrect, this requirement may cause leakage issue due to boundary condition. Origin supports the use of window functions to mitigate the leakage. Several functions are supported, including Triangular, Bartlett, Hann, Hamming and so on. Although these window functions can deal with the boundary condition, the interference of low-frequency diffusion still occurs to the spectrum, and the effect of adding a window affects energy estimation in main frequencies. Thus, the influence of trend signal cannot be easily resolved in the frequency domain directly or indirectly. A thorough solution lies in obtaining the real trend signal from the original signal.